Hiring Data Scientists? Here’s What Recruiters Need To Know

Emily Smykal June 1, 2016

Big data has become an overwhelming presence in a growing number of workplaces. But who exactly is handling that data? Data scientists have proliferated in the twentieth century as analysts and number wranglers in every business from tech to retail. Typically required to manage and process large amounts of information, data scientists rely on analytical skills and advanced software to understand and present their findings.

It’s become a fast paced job, as businesses rely more and more on accurate, actionable data to drive decision-making and gain a competitive edge. So it’s no surprise data science as a career is facing a shortage of workers. Back in 2011, McKinsey & Company predicted a shortfall of 1.5 million analysts and data science managers by 2018. And a 2015 MIT Sloan Management Review survey found 40% of the companies who responded struggled to source and retain data analytics candidates.

Recruiters who are competing for data science talent should take a moment to better understand these candidates. A recent survey from AnalyticsWeek and Business Over Broadway of over 1,000 data science professionals sheds some much needed light on the profession across different industries.

Where Do Data Scientists Work?

Data science is often associated with tech companies and fast growing startups. But these professionals are also in demand in industries including healthcare, government, and travel. The survey found that 26% of respondents work in IT, but 14% are employed in science-based academic and research roles, and 13% work in consulting.

The survey also broke down data scientists by the role they serve within their industry. Among data scientists in IT, for example, 57% report working as developers. But the most common role for data scientists in education, advertising and financial services is a research position. Retail data scientists are more likely to serve as business professionals, and their counterparts in the communications industry work in a more creative role.

What Skills Do Data Scientists Have?

Survey respondents were also asked to rate their proficiency for a variety of data science skills. While most industries followed a general pattern, a few results stood out. In every industry sampled except education/sciences, data scientists claimed greater proficiency in business and math/statistics skills than actual technology skills.

The highest levels of proficiency came from data scientists in professional services, while the lowest levels were reported by those working in advertising/media, and education/sciences excluding technology skills.

The survey dug a bit deeper into more specific technological skills, but also soft skills that many employers value. Communications skills, for instance, are more likely to be reported as proficient by researchers, creatives, and managers than by their data science colleagues who work as developers. Project management is another key skill recruiters should look for in a data scientists, and the highest rates of proficiency here were reported among creatives and managers.

Data Scientists and Job Satisfaction

An important factor when it comes to retaining top talent, including data scientists, is their satisfaction at work. The survey asked respondents to rate their satisfaction with the outcomes of their analytics projects, on a scale of 0 to 10. Data scientists in education/science reported the highest level of satisfaction (7.5) while those working in communications reported the lowest (6.6).

Besides overall satisfaction, the survey also looked at the reported satisfaction among data scientists in different job levels. Directors claimed to feel the most satisfied with project outcomes, followed closely by managers, but individual contributors and C-suite data scientists weren’t far behind.

Where Does this Leave Recruiters?

No matter how many data scientists you need to bring on board, you want the best and brightest handling your expanding supply of data. Some talent acquisition teams will simply poach top analysts from competitors or other industries, offering incentives to make the switch. Others may choose to train data scientists from within their ranks, providing educational credits for master’s programs and online courses.

Any effort to attract and retain data scientists will need to keep their analytical methods and their technological needs in mind. What’s the point in posting a job for an experienced data analyst if your organization hasn’t invested in the hardware and software to process data? Recruiters should first evaluate the resources they have in place that a data scientist would need, then be sure to advertise these as benefits of the job. There is perhaps nothing more frustrating for these candidates than being asked to synthesize data without the right tools to do so.